1. Mission

• High-quality human resource training: Provide students and learners with both fundamental and advanced knowledge in Data Science and Artificial Intelligence to meet the demands of the digital transformation era.
• Research and development: Promote scientific research activities and apply advanced technologies to real-world problems, contributing to solutions in social, economic, and technical domains.
• Technology transfer: Support enterprises and organizations in deploying data-driven and AI-based solutions, enhancing national competitiveness.
• Community development: Build collaborative networks among researchers, students, and industry partners to disseminate knowledge and foster technology adoption.

2. Educational Objectives

The Department of Data Science aims to achieve the following educational goals:

• Domain knowledge: Provide a solid foundation in mathematics (linear algebra, probability and statistics), programming, machine learning, deep learning, big data processing, and AI techniques such as Natural Language Processing (NLP) and Computer Vision.
• Practical skills: Develop competencies in data analysis, AI model design, real-world application deployment, and interdisciplinary problem-solving.
• Research capability: Encourage student participation in scientific research, fostering creativity and lifelong learning to adapt to rapid technological changes.
• Professional ethics: Educate students on social responsibility and ethical issues in AI (e.g., data privacy, algorithmic fairness).

3. Research Areas

The department focuses on the following research directions:

• Machine Learning and Deep Learning: Development of optimization algorithms and deep neural networks for applications such as image recognition and trend prediction.
• Natural Language Processing (NLP): Research on chatbots, machine translation, sentiment analysis, and Vietnamese language understanding systems.
• Computer Vision: Applications in facial recognition, video analysis, and automation systems.
• Big Data Science: Large-scale data analysis and visualization, data mining, and applications in business, healthcare, and education.
• Interdisciplinary AI: Integration of AI in domains such as healthcare (disease diagnosis), agriculture (weather forecasting), and finance (market prediction).
• Ethical and Sustainable AI: Research on transparency, fairness, and the societal impact of AI.

4. Collaboration

The department collaborates with universities, research institutes, and technology enterprises both domestically and internationally in education and scientific research. Key areas include:

• Big Data
• Deep Learning
• Machine Learning
• Data Mining
• Internet of Things (IoT)
• Computer Vision

5. Faculty Members

Dr. The Bao Phung (Head of Department)

Dr. Thanh Long Nguyen (Vice Dean)

Dr. Duong Ha Ngo

Dr. Viet Hung Tran

M.Sc. Chau Lan Huynh Thi

M.Sc. Ngoc Mai Phan Thi

M.Sc. Trong Nghia Dinh Nguyen

M.Sc. Thuy Trang Nguyen Thi

M.Sc. Van Tho Tran

M.Sc. Dinh Toan Tran (Academic Club Coordinator)

M.Sc. Huyen Trang Nguyen Thi

6. Programs Offered

• Undergraduate programs.

7. Career Opportunities

Graduates of the program may pursue careers such as:

• Data Scientist: Analyze large datasets and develop predictive models.
• AI Engineer: Design and deploy intelligent AI systems.
• Data Engineer: Build data infrastructure and manage data pipelines.
• Business Analyst: Apply data-driven insights to optimize business strategies.
• AI Researcher: Work in research institutes, universities, or R&D departments.
• Entrepreneurship: Develop AI-based products and services in healthcare, education, e-commerce, and more.

Additionally, graduates can pursue higher education and contribute to key sectors of Vietnam’s digital economy.

8. Extracurricular Activities

• Programming and AI competitions: ACM-ICPC, AI hackathons, data analysis contests, Euréka research competition, and institutional research activities.
• Workshops and seminars: Inviting experts from industry and research institutions to share insights on emerging technologies.
• Student clubs: Academic clubs where students practice projects and exchange knowledge.
• Internships and real-world projects: Collaboration with enterprises to provide hands-on experience.
• International exchange: Student exchange programs, international conferences, and scholarship opportunities.
• Technology events: Showcasing AI applications and student projects.
• Social and volunteer activities: Participation in community campaigns and professional activities within the university.